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Relevant bibliographies by topics / Security of machine learning classifiers

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Journal articles

Academic literature on the topic 'Security of machine learning classifiers'

Author: Grafiati

Published: 14 March 2023

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Journal articles on the topic "Security of machine learning classifiers"

1

Atnafu, Surafel Mehari, and Prof (Dr ). Anuja Kumar Acharya. "Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (2021): 22–28. http://dx.doi.org/10.35940/ijainn.b1025.041221.

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In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an eff

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2

Atnafu, Surafel Mehari, and Prof (Dr ). Anuja Kumar Acharya. "Comparative Analysis of Intrusion Detection Attack Based on Machine Learning Classifiers." Indian Journal of Artificial Intelligence and Neural Networking 1, no. 2 (2021): 22–28. http://dx.doi.org/10.54105/ijainn.b1025.041221.

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Abstract:

In current day information transmitted from one place to another by using network communication technology. Due to such transmission of information, networking system required a high security environment. The main strategy to secure this environment is to correctly identify the packet and detect if the packet contains a malicious and any illegal activity happened in network environments. To accomplish this, we use intrusion detection system (IDS). Intrusion detection is a security technology that design detects and automatically alert or notify to a responsible person. However, creating an eff

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3

ALGorain, Fahad T., and John A. Clark. "Covering Arrays ML HPO for Static Malware Detection." Eng 4, no. 1 (2023): 543–54. http://dx.doi.org/10.3390/eng4010032.

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Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with newly evolving malware. Four machine learning techniques were tuned using cAgen, a tool for generating covering arrays. The results show that cAgen is an efficient approach to achieve the optimal parameter choices for ML techniques. Moreover, the covering array shows a sig

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4

Katzir, Ziv, and Yuval Elovici. "Quantifying the resilience of machine learning classifiers used for cyber security." Expert Systems with Applications 92 (February 2018): 419–29. http://dx.doi.org/10.1016/j.eswa.2017.09.053.

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5

Gongada, Sandhya Rani, Muktevi Chakravarthy, and Bhukya Mangu. "Power system contingency classification using machine learning technique." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3091–98. http://dx.doi.org/10.11591/eei.v11i6.4031.

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One of the most effective ways for estimating the impact and severity of line failures on the static security of the power system is contingency analysis. The contingency categorization approach uses the overall performance index to measure the system's severity (OPI). The newton raphson (NR) load flow technique is used to extract network variables in a contingency situation for each transmission line failure. Static security is categorised into five categories in this paper: secure (S), critically secure (CS), insecure (IS), highly insecure (HIS), and most insecure (MIS). The K closest neighb

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6

Mehanović, Dželila, and Jasmin Kevrić. "Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection." Traitement du Signal 37, no. 4 (2020): 563–69. http://dx.doi.org/10.18280/ts.370403.

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Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for per

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7

Deshmukh, Miss Maithili, and Dr M. A. Pund. "Implementation Paper on Network Data Verification Using Machine Learning Classifiers Based on Reduced Feature Dimensions." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2921–24. http://dx.doi.org/10.22214/ijraset.2022.41938.

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Abstract: With the rapid development of network-based applications, new risks arise and extra security mechanisms require additional attention to enhance speed and accuracy. Although many new security tools are developed, the rapid rise of malicious activity may be a major problem and therefore the ever-evolving attacks pose serious threats to network security. Network administrators rely heavily on intrusion detection systems to detect such network intrusion activity. a serious approach is machine learning methods for intrusion detection, where we learn models from data to differentiate betwe

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8

Runwal, Akshat. "Anomaly based Intrusion Detection System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 255–60. http://dx.doi.org/10.22214/ijraset.2021.37955.

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Abstract: Attacks on the computer infrastructures are becoming an increasingly serious issue. The problem is ubiquitous and we need a reliable system to prevent it. An anomaly detection-based network intrusion detection system is vital to any security framework within a computer network. The existing Intrusion detection system have a high detection rate but they also have mendacious alert rates. With the use of Machine Learning, we can implement an efficient and reliable model for Intrusion detection and stop some of the hazardous attacks in the network. This paper focuses on detailed study on

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9

Abdulrezzak, Sarah, and Firas Sabir. "An Empirical Investigation on Snort NIDS versus Supervised Machine Learning Classifiers." Journal of Engineering 29, no. 2 (2023): 164–78. http://dx.doi.org/10.31026/j.eng.2023.02.11.

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With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to

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10

Singh, Ravi, and Virender Ranga. "Performance Evaluation of Machine Learning Classifiers on Internet of Things Security Dataset." International Journal of Control and Automation 11, no. 5 (2018): 11–24. http://dx.doi.org/10.14257/ijca.2018.11.5.02.

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